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Combining two open source tools for neural computation (BioPatRec and Netlab) improves movement classification for prosthetic control

Overview of attention for article published in BMC Research Notes, August 2016
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Title
Combining two open source tools for neural computation (BioPatRec and Netlab) improves movement classification for prosthetic control
Published in
BMC Research Notes, August 2016
DOI 10.1186/s13104-016-2232-y
Pubmed ID
Authors

Cosima Prahm, Korbinian Eckstein, Max Ortiz-Catalan, Georg Dorffner, Eugenijus Kaniusas, Oskar C. Aszmann

Abstract

Controlling a myoelectric prosthesis for upper limbs is increasingly challenging for the user as more electrodes and joints become available. Motion classification based on pattern recognition with a multi-electrode array allows multiple joints to be controlled simultaneously. Previous pattern recognition studies are difficult to compare, because individual research groups use their own data sets. To resolve this shortcoming and to facilitate comparisons, open access data sets were analysed using components of BioPatRec and Netlab pattern recognition models. Performances of the artificial neural networks, linear models, and training program components were compared. Evaluation took place within the BioPatRec environment, a Matlab-based open source platform that provides feature extraction, processing and motion classification algorithms for prosthetic control. The algorithms were applied to myoelectric signals for individual and simultaneous classification of movements, with the aim of finding the best performing algorithm and network model. Evaluation criteria included classification accuracy and training time. Results in both the linear and the artificial neural network models demonstrated that Netlab's implementation using scaled conjugate training algorithm reached significantly higher accuracies than BioPatRec. It is concluded that the best movement classification performance would be achieved through integrating Netlab training algorithms in the BioPatRec environment so that future prosthesis training can be shortened and control made more reliable. Netlab was therefore included into the newest release of BioPatRec (v4.0).

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 37 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 37 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 10 27%
Student > Master 8 22%
Student > Ph. D. Student 6 16%
Student > Doctoral Student 3 8%
Researcher 2 5%
Other 5 14%
Unknown 3 8%
Readers by discipline Count As %
Engineering 16 43%
Medicine and Dentistry 10 27%
Computer Science 5 14%
Physics and Astronomy 1 3%
Neuroscience 1 3%
Other 1 3%
Unknown 3 8%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 19 December 2016.
All research outputs
#11,135,222
of 12,519,627 outputs
Outputs from BMC Research Notes
#2,347
of 2,804 outputs
Outputs of similar age
#296,303
of 361,813 outputs
Outputs of similar age from BMC Research Notes
#243
of 290 outputs
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We're also able to compare this research output to 290 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.